Recently, Quality Estimation (QE) has become of growing importance in the field of machine translation and cross-lingual corpus filtering, which aims to directly assess the quality of a source-target translation pair, without referencing to golden human translations, with typically two goals: word-level and sentence-level post-editing effort estimations [1]}, [2]}, [3]}.
The goal of word-level QE is to predict a tag for each token in the translated sentence to detect the mistranslated words and a tag for each gap between two tokens to detect missing words.
On the other hand, in sentence-level QE tasks, a commonly used measurement metric is the
Human Translation Error Rate (HTER) score [4]}, which is the ratio between the number of edits needed and the reference translation length.
Table REF  gives a data example from the WMT20 English-Chinese QE dataset, where the accurate translations are shown in green and the bad translations are marked in red.
